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A Doubly Robust Censoring Unbiased Transformation


  • Rubin Daniel

    (University of California, Berkeley)

  • van der Laan Mark J.

    (Division of Biostatistics, School of Public Health, University of California, Berkeley)


We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data.

Suggested Citation

  • Rubin Daniel & van der Laan Mark J., 2007. "A Doubly Robust Censoring Unbiased Transformation," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-21, March.
  • Handle: RePEc:bpj:ijbist:v:3:y:2007:i:1:n:4

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